Pt Catalysts Supported on H<sub>2</sub> and O<sub>2</sub> Plasma-Treated Al<sub>2</sub>O<sub>3</sub> for Hydrogenation and Dehydrogenation of the Liquid Organic Hydrogen Carrier Pair Dibenzyltoluene and Perhydrodibenzyltoluene
Bibliographic record
Abstract
Dibenzyltoluene (DBT) is a promising liquid organic hydrogen carrier (LOHC) with theoretical 6.2 wt % hydrogen storage capacity which can be coupled with a renewable energy power generation system. In this work, the surface hydroxyl groups and surface oxygen vacancies (SOVs) on alumina were modified by a convenient and environmentally friendly plasma treatment method. Different Pt/Al 2 O 3 catalysts were prepared via impregnation of the treated alumina, and the effects of different surface hydroxyl groups and SOVs on their reactivity for the reversible hydrogenation and dehydrogenation of DBT were investigated. The results show that SOVs increased after H 2 plasma treatment, whereas the surface hydroxyl groups increased and SOVs decreased after O 2 plasma treatment. Both the surface hydroxyl group and SOV can improve Pt metal dispersion. The more interesting observation is that the hydroxyl groups promote hydrogen spillover and the proportion of Pt(0), which not only benefit the catalyst hydrogenation and dehydrogenation activity but also reduce side reactions and increase long-term cycle performance. However, increased SOVs increased the fraction of low coordinated Pt which reduces the long-term cycle performance of the catalyst. As a result, increasing surface hydroxyl groups and appropriately reducing SOVs on Pt/Al 2 O 3 are propitious for improving both reactivity and long-term cycle performance when using DBT as a LOHC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".